[1]卢晓光1,李凤格2.基于数字孪生的风机实时载荷预估研究[J].机械与电子,2021,(06):24-28.
 LU Xiaoguang,LI Fengge.Real Time Load Prediction of Wind Turbine Based on Digital Twin[J].Machinery & Electronics,2021,(06):24-28.
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基于数字孪生的风机实时载荷预估研究()
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机械与电子[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2021年06期
页码:
24-28
栏目:
机电一体化技术
出版日期:
2021-06-23

文章信息/Info

Title:
Real Time Load Prediction of Wind Turbine Based on Digital Twin
文章编号:
1001-2257 ( 2021 ) 06-0024-05
作者:
卢晓光 李凤格
1. 许昌许继风电科技有限公司,河南 许昌 461000 ;?
2. 许昌智能继电器股份有限公司,河南 许昌 461000
Author(s):
LU Xiaoguang LI Fengge
(1.Xuchang XJ Wind Power Technology Co., Ltd. , Xuchang 461000 , China ;
2.Xuchang Intelligent Relay Co., Ltd. , Xuchang 461000 , China)
关键词:
数字孪生预估载荷权重矩阵风电机组实时控制
Keywords:
digital twins estimated load weight matrix wind turbine real-time control
分类号:
TM614
文献标志码:
A
摘要:
为推动风电产业技术的持续进步,改善风机控制系统性能,利用数字孪生方式,实现风机实时载荷的预估.利用风机原有的高可靠采集信号作为输入,搭建风电机组回归模型,建立系统权重矩阵,并进行线性插值使模型适应整个发电工况,完成了风电机组载荷实时预估.载荷预估值可替代目前的应变片载荷测试系统.载荷预估经评估计算精确度在 96.00% 以上.预估载荷在风机塔架推力消减实时控制系统中的应用表明,减载效果显著.
Abstract:
In order to promote the continuous progress of wind power industry technology and improve the performance of wind turbine contro lsystem , the real-time load prediction of wind turbine is realized by using digital twin mode.Using the original high reliable acquisition signal of wind turbine as input , the regression model of wind turbine is built , the system weight matrix is established , and the linear interpolation is carried out to make the model adapt to the whole power generation condition , and the real-time load prediction of wind turbine is completed.The load prediction value can replace the current strain gauge load test system.The accuracy of load prediction is more than 96.00%.The application of load prediction in real-time control system of wind turbine tower thrust reduction shows that the effect of load reduction is remarkable.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2021-02-19
基金项目:国家重点研发计划项目( 2018YFB0904000 );国家电网公司总部科技项目(52153218000H )
作者简介:卢晓光 (1983-),男,河南许昌人,硕士,高级工程师,主要从事机电一体化方向设计与研发,通信作者.
更新日期/Last Update: 2021-06-21